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Denitrification

Denitrification is the process by which nitrate (NO3-) is reduced to nitrite (NO2-) and then further reduced to nitric oxide (NO), nitrous oxide (N2O), and finally to nitrogen gas (N2).
This anaerobic respiratory process is carried out by a wide variety of bacteria, archaea, and some eukaryotes, and plays a crucial role in the global nitrogen cycle.
Denitrification helps to remove excess nitrates from the environment, preventing eutrophication and supporting healthy ecosysyems.
Understanding the mechanisms and factors influencing denitrification is essential for managing nitrogen pollution, improving agricultural practices, and predicting greenhouse gas emissions.
PubCompare.ai empowers denitrification research by helping users locate the best protocols from literature, pre-prints, and patents, elevating reproducibility and accuracy to optimize these important studies.

Most cited protocols related to «Denitrification»

Building a Curated Functional Gene Database

To address these challenges we aimed to build a manually curated and validated database for screening of environmental metagenomic and metatranscriptomic sequence datasets for functional genes. We focused on biochemical functions and metabolic pathways important in environmental microbial ecology, including global carbon and nitrogen cycles, by manually selecting and organizing functional gene information into a database here called ‘FOAM’ (Functional Ontology Assignments for Metagenomes).
First, KEGG orthologs (KOs) (12 (link)) were retrieved to fit within a hierarchical organization from general features to specific pathways (such as denitrification, methanogenesis, etc.). KEGG KO (a reference set of homologous genes, consistent in known functions) benefits from stability, good maintenance, curation, and third party annotation. The KEGG KO was chosen as the FOAM ‘unit’ because it is a qualitative and dynamically maintained knowledge base associated with a rich tool environment that is available within or outside of KEGG. Additionally, using KEGG KO permits the use of all visualization KEGG tools or third party software that have been released [e.g. Cytoscape (14 ), Glamm (15 (link)), Voronto (16 (link)), iPATH (17 (link)), bioconductor Pathview package (18 (link))]. KEGG KO lists the genes defined in KEGG that belong to each functional and homologous family and, as a consequence, these can be multi-domain and multi-functional. Here, to provide accurate functional annotation, each FOAM module was constructed to ideally target one function.
The reduced size of the resultant FOAM database, compared to non-specific sequence databases, was a first step towards significant improvement in the speed and specificity of similarity searches. In addition, to improve upon the sensitivity of conventional heuristic alignment programs, we turned each KO set into Hidden Markov Models (HMMs; 19 (link)) by fetching their corresponding protein family (Pfam) profiles (20 (link)) as described in Figure 1. This step generated a sizeable number of conflicts (several Pfam per KO and vice versa) that were automatically resolved by functional assignments to KO. For the few remaining unresolved assignations, the corresponding set of sequences was manually split according to the topology of their phylogenetic trees. At this point the HMMs were re-trained from the new pool of sequences.
By retrieving the sequences of the corresponding Pfam of each selected KO, in addition to the sequences already present in the FOAM database, we ensured precise detection of functions from potentially distant homologs. With this method, ∼74 000 peptide sequence profiles were specifically tailored and trained to predict functions as defined in KEGG KO. This profile-based searching approach enabled identification of less conserved regions along sequence alignments. Thus this method is applicable for searching for more distant homologs, similar to the approach used by Pfam (20 (link)) and TIGRFAM (21 (link)). However, we found that most Pfam and TIGRFAM models provide multiple KEGG KO assignments and did not serve our needs for retrieval of functionally specific annotations from metagenomes. Also, Pfam and TIGRFAM do not focus on environmental processes and cover only few functions of interest for different environmental sources. Additionally, Pfam and TIGRFAM are based on a simplified alignment, called ‘SEED’, which is composed of a collection of sequences representative of a protein family, whereas our aim was a more comprehensive recruitment of more distant homologs. Recently, FunGene (22 (link)) was published as a new toolkit specialized to process amplicon data for functional genes, focusing on marker genes (∼100 currently available). FunGene provides users with HMMs for their marker genes of interest as a tool to test primers and probes. Moreover, FunGene allows users to build and submit new HMMs. FOAM is complementary to FunGene: it includes ∼3000 custom protein models obtained by enriching Pfams relevant to environmental microbiology with more protein sequences. An additional attribute of FOAM is that KO assignments were screened during the manual calibration to ensure that the Pfam alignments all targeted the same KO. If parts of the alignments targeted other KOs they were omitted from building the models or manually reassigned. Importantly, FOAM is a database that can be complemented with input from the user community. The FOAM database is by no means complete and we encourage recommendations from future users for additional categories to input into FOAM.

Prestat E., David M.M., Hultman J., Taş N., Lamendella R., Dvornik J., Mackelprang R., Myrold D.D., Jumpponen A., Tringe S.G., Holman E., Mavromatis K, & Jansson J.K. (2014). FOAM (Functional Ontology Assignments for Metagenomes): a Hidden Markov Model (HMM) database with environmental focus. Nucleic Acids Research, 42(19), e145.

Publication 2014

Amino Acid Sequence Carbon Denitrification Environmental Microbiology Genes Genetic Markers Hypersensitivity Hypertelorism, Severe, With Midface Prominence, Myopia, Mental Retardation, And Bone Fragility Metagenome Methanobacteria Nitrogen Cycle Oligonucleotide Primers Peptides Proteins Sequence Alignment

Comprehensive Database of Microbial Denitrification Genes

A local database was constructed by downloading all 4135 draft and completed microbial genome nucleotide sequences available (November 2012) at the National Center for Biology Information (NCBI, www.ncbi.nlm.nih.gov). To ensure that homology searches were as comprehensive as possible, a two-step procedure was performed for each gene. First, an initial TBLASTN search [30] (link) of the online NCBI microbial genomes database (www.ncbi.nlm.nih.gov/sutils/genom_table.cgi) was performed using translated nirS, nirK, and nosZ gene sequences from either Paracoccus denitrificans PD1222 or Bradyrhizobium japonicum USDA110 as queries. Resulting hits were then translated to amino acid sequences and aligned using SATÉ v2.2.3 [31] (link) with MAFFT [32] (link) as aligner, MUSCLE as merger and RAxML [33] (link) as the tree estimator. Gene identity of the retrieved sequences was confirmed by examining the amino acid alignments in relation to characterized homologs, with emphasis on conserved positions crucial for protein functioning and phylogenetic inference (see below). The resulting amino acid alignments of nirK, nirS and nosZ, with 477, 150 and 282 sequences, respectively, were then used to create Position Specific Score Matrices (PSSM) [34] (link) for conducting a more comprehensive PSI-TBLASTN search of the downloaded database. Truncated sequences and sequences with stop codons were excluded, and redundancy in the data set was reduced by eliminating different strains of the same species with identical nirK, nirS and nosZ amino acid sequences. Strains with identical sequences were kept when a unique co-occurrence pattern of denitrification genes was observed, or when the sequence of another denitrification gene was not identical, resulting in a dataset of 652 organisms (see Table S1 for species name, NCBI taxon ID, project name). We then searched the final set of genomes for homologues of the qnorB and cnorB variants of the NO-reductase. This was performed in a similar manner as described for the nir and nos genes, with the exception that the PSSM was generated by downloading the 10 most diverse representative cNorB and qNorB amino acid sequences from the NCBI conserved domains database (http://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml) to allow for an equal representation of both variants within the initial PSSM. For the eukaryotic species, the amino acid sequence for the P450_nor from Fusarium oxysporum[35] (link) was used as a query for TBLASTN searches of each fungal genome, and the resulting hits were aligned to the query sequence to both correctly identify P450_nor based on previously described conserved amino acid positions [28] (link), as well as to aid in assembly of exons.
Small subunit (SSU) rRNA gene sequences corresponding to the organisms were retrieved from the local genome database using Infernal [36] (link). In cases where there was more than one SSU rRNA gene sequence in a genome, the longest sequence was chosen. Taxonomic assignment was based on NCBI classification, which was verified by classification of SSU sequences using the SILVA database [37] (link). In addition, habitat and isolation source was either downloaded from the Genomes online database (GOLD, 2012 November 15, www.genomesonline.org/) [29] (link) or searched for in NCBI using the taxon ID of the respective genome and looking at connected publications when available.

Graf D.R., Jones C.M, & Hallin S. (2014). Intergenomic Comparisons Highlight Modularity of the Denitrification Pathway and Underpin the Importance of Community Structure for N2O Emissions. PLoS ONE, 9(12), e114118.

Publication 2014

Amino Acids Amino Acid Sequence Base Sequence Bradyrhizobium japonicum Codon, Terminator Denitrification Eukaryota Exons Fusarium oxysporum Genes Genes, vif Genome Genome, Fungal Genome, Microbial Gold isolation Muscle Tissue Oxidoreductase Paracoccus denitrificans Proteins Protein Subunits Ribosomal RNA Genes Spectroscopy, Near-Infrared Strains Trees

Activated Sludge Microbiome Profiling and Associations

Microbial community analyses were performed for samples collected by the MiDAS global consortium. Only activated sludge plants, which include conventional activated sludge (CAS) and sequence batch reactors (SBR), were chosen for detailed analyses. We further selected plants designed for carbon removal (C), carbon removal with nitrification (C,N), carbon removal with nitrification and denitrification (C,N,DN) and carbon removal with nitrogen removal and enhanced biological phosphorus removal, EBPR (C,N,DN,P). Samples with less than 10,000 reads were discarded, providing in total 861 V1–V3 samples and 666 V4 samples.
Associations between the activated sludge microbiota and the following process-related or environmental variables were investigated: process type (as listed above); industrial load (expressed as a fraction of the influent COD); temperature in the process tank (°C), continent and Köppen–Geiger climate classification^{63 (link)}. Industrial load and temperature was treated as a discrete variable with the following ranges applied: very low (1.8–10.0 °C), low (10.1–15.0 °C), moderate (15.1–20.0 °C), high (20.1–25.0 °C), very high (25.1–30.0 °C), extremely high (30.1–38.0 °C) for temperature; and none (0%), very low (1–10%), low (11–29%), medium (30–50%), high (51–99%), all (100%) for the industrial load. The following climate zone groups were used in the analyses: A: tropical/megathermal climates, B: dry (desert and semi-arid) climates, C: temperate/mesothermal climates, D: continental/microthermal climates, E: polar climates.
For alpha diversity analyses, samples were rarefied to 10,000 reads, and alpha diversity (Observed taxa and inverse Simpsons) was calculated using the ampvis2 package⁶⁰. The Kruskal–Wallis with Dunn’s post hoc test (Bonferroni correction with ɑ = 0.01 before correction) was used to determine statistically significant differences in alpha diversity between samples grouped by process and environmental variables.
Distance decay relationship was determined using untransformed values of geographic distance against microbial community similarity distance (Bray–Curtis, or Soerensen) for ASVs, 97% OTUs and genera. Geographical distances between samples were calculated using the distm function in the geosphere R package⁶⁴ using the Haversine formula. To examine the strength of correlation between geographic and community distance matrices, the Mantel test using Spearman correlation and 999 permutations was performed using the mantel function in the vegan R package⁵⁸.
Beta-diversity distances based on Bray–Curtis (abundance-based) and Soerensen (occurrence-based) for genera (relative genus abundance >0.01% for Soerensen diversity) was calculated using the vegdist function in the vegan R package⁵⁸ and visualised by PCoA and RDA plots with the ampvis2 package⁶⁰. Individual process or environmental variables were used as constraints for the RDA. To determine how much individual parameters affected the structure of the microbial community across the WWTPs, a permutational multivariate analysis of variance (PERMANOVA) test was performed on the beta-diversity matrices using the adonis function in the vegan package with 999 permutations.
Core genera and species were defined based on their relative abundances in individual WWTPs. Taxa were defined as abundant when present at >0.1% relative abundance in individual WWTPs. Based on how frequently taxa were observed to be abundant, we defined the following core communities: loose core (>20% of WWTPs), general core (>50% of WWTPs), and strict core (>80% of WWTPs). Additionally, we defined conditionally rare or abundant taxa (CRAT)^{30 (link)} composed of taxa present in one or more WWTPs at >1% relative abundance, but not belonging to the core taxa.

Dueholm M.K., Nierychlo M., Andersen K.S., Rudkjøbing V., Knutsson S., Albertsen M, & Nielsen P.H. (2022). MiDAS 4: A global catalogue of full-length 16S rRNA gene sequences and taxonomy for studies of bacterial communities in wastewater treatment plants. Nature Communications, 13, 1908.

Publication 2022

Adonis asunaprevir Biopharmaceuticals Carbon Climate Denitrification GOLPH3 protein, human Microbial Community Microbial Community Structure Nitrification Nitrogen Phosphorus Plants Sludge Tropical Climate Vegan

Apoptosis Assay for Biofilm Cells

Annexin V-fluorescein isothiocyanate (FITC) and propidium iodide (PI) stains were used to determine the percentage of cells within the biofilm undergoing apoptosis and necrosis on the electrodes during the bioelectrochemical denitrification operation. An apoptosis assay was conducted using the protocol supplied by the manufacturer BioVision, Inc. The cells were gently removed from the electrodes for a brief time, re-suspended with 500 μL of 1× binding buffer, and then treated with 5 μL of Annexin V-FITC and 5 μL PI. Immediately after a 5-min incubation in the dark at room temperature, each sample was analyzed using a FACSCalibur™ flow cytometer with the supplied software as the instrument. The Annexin V-FITC binding (Ex = 488 nm; Em = 530 nm) was analyzed using a FITC signal detector, and PI staining was analyzed by a phycoerythrin emission signal detector. A cytogram analysis was performed using the FLOW software version 2.4.1; the unstained cells were classified as “live,” “Left Bottom,” or “Q1 area.” Meanwhile, cells stained with Annexin V only were classified as “early apoptotic,” “Left Top,” or “Q2 area”; cells stained by both Annexin V and PI were classified as “late apoptotic,” “Right Top,” or “Q3”; cells stained by PI only were classified as “dead,” “Right Bottom,” or “Q4” cells.

Hoseinzadeh E., Wei C., Farzadkia M, & Rezaee A. (2020). Effects of Low Frequency-Low Voltage Alternating Electric Current on Apoptosis Progression in Bioelectrical Reactor Biofilm. Frontiers in Bioengineering and Biotechnology, 8, 2.

Publication 2020

Annexin A5 Apoptosis Biofilms Biological Assay Buffers Cells Denitrification Fluorescein Fluorescein-5-isothiocyanate isothiocyanate Necrosis Phycoerythrin Propidium Iodide

Modeling Plant-Microbe Interactions

We constructed our a priori models based on current knowledge on plant-microbe-functioning interactions and tested whether the data fit these models using the standard and the multigroup modelling approach in the R library lavaan. We used model modification indices and stepwise removal of non-significant relationships as in De Vries and Bardgett^{61 (link)}. We used a minimum set of parameters to assess model fit, including root mean square error of approximation (RMSEA), and comparative fit index (CFI). We used PCOA axis 1 scores as a proxy for fungal and bacterial community composition, PCA axis 1 scores as a proxy for plant community composition, and the ratio between the sum of nirS and nirK and the sum of nosZI and nosZII gene abundances to indicate relative changes in genetic potential for the soil functions denitrification and N₂O reduction.

de Vries F.T., Griffiths R.I., Bailey M., Craig H., Girlanda M., Gweon H.S., Hallin S., Kaisermann A., Keith A.M., Kretzschmar M., Lemanceau P., Lumini E., Mason K.E., Oliver A., Ostle N., Prosser J.I., Thion C., Thomson B, & Bardgett R.D. (2018). Soil bacterial networks are less stable under drought than fungal networks. Nature Communications, 9, 3033.

Publication 2018

Bacteria cDNA Library Denitrification Epistropheus Genes Microbial Interactions Plant Roots Plants Spectroscopy, Near-Infrared

Most recents protocols related to «Denitrification»

Phylogenetic Analysis of N-Metabolic Genes

N-metabolic genes were selected, and alignment of these genes was performed. The top three similar gene sequences of nitrate assimilation and denitrification were retrieved after doing BLASTP against the NCBI Nr database for the sequence alignment. Sequence Manipulation Suite version 2 was used for alignment and polished the protein sequences [20 (link)]. All the protein sequences of N-metabolism genes (assimilatory and respiratory nitrate reductase, nitrite reductase, nitric oxide reductase, hydroxylamine reductase, and glutamine synthetase) of Lelliottia amnigena and their similarities genes were analyzed by BLASTP and saved in FASTA format as an input file. To investigate the phylogenetic relationship of selected nitrogen metabolism genes was performed with the help of the MEGA 11(Mega Evolutionary Genetic Analysis version 11) tool. First, the protein sequence was aligned with MUSCLE and phylogenetic tree was constructed based on neighbor-joining [21 (link)]. The percentage of bootstrap [22 (link)] values were shown at the nodes. The evolutionary distances were computed using the Jones Taylor Thornton method [23 (link)] and are in the units of the number of amino acids substitutions per site. Branch length are given below the node. It defines the genetic changes i.e., longer the branch more genetic changes.

Thakur P, & Gauba P. (2023). Identification and examination of nitrogen metabolic genes in Lelliottia amnigena PTJIIT1005 for their ability to perform nitrate remediation. BMC Genomics, 24, 104.

Publication 2023

Amino Acid Sequence Amino Acid Substitution Biological Evolution Denitrification Genes Glutamate-Ammonia Ligase hydroxylamine reductase Lelliottia amnigena MEGA 11 Metabolism Muscle Tissue Nitrate Reductase Nitrates nitric oxide reductase Nitrite Reductase Nitrogen nucleoprotein, Measles virus Reproduction Sequence Alignment

Agricultural Greenhouse Gas Emissions Protocol

Nitrous oxide flux was analyzed using the closed chamber method (Herr et al., 2020 (link)). In this method, dark PVC boxes were installed, and the samples were drawn every 24 h in the morning using syringes, evacuated into plastic vials, and analyzed chromatographically. Denitrification losses were estimated by the denitrification enzyme assay method described by Smith & Tiedje (1979) (link).
Soil urease activity was analyzed at the 50% flowering stage, calorimetrically, by Bremner & Douglas (1971) (link) method. The normalized difference vegetation index (NDVI) was measured using a green seeker (handheld crop sensor by Trimble, Westminster, CO, USA) at the 50% flowering stage. Infrared gas analyzer (LI-COR Model LI-6400X7 portable photosynthetic system) (IRGA) was used to measure the photosynthetic rate and stomatal conductance. Soil microbial biomass carbon (MBC) and soil microbial biomass nitrogen (MBN) were determined by the chloroform fumigation–extraction method described by Vance, Brookes & Jenkinson (1987) (link) and Brookes et al. (1985) (link), respectively. The N content in grains and straws was also measured using the Kjeldahl method (Kjeldahl, 1883 (link)). After harvesting the crop, yield attributes were calculated from each plot.

L. Ramalingappa P., Shrivastava M., Dhar S., Bandyopadhyay K., Prasad S., Langyan S., Tomer R., Khandelwal A., Darjee S, & Singh R. (2023). Reducing options of ammonia volatilization and improving nitrogen use efficiency via organic and inorganic amendments in wheat (Triticum aestivum L.). PeerJ, 11, e14965.

Publication 2023

Carbon Cereals Chloroform Crop, Avian Denitrification Enzyme Assays Fumigation Nitrogen Oxide, Nitrous Photosynthesis Surgical Stoma Syringes Urease

Impacts of Hyphal Exudates on N2O Production

A sealed serum bottle assay was conducted to examine the effects of hyphal exudates and major compounds on net N₂O production by P. fluorescens JL1. Hyphal exudate was applied as one treatment. Fructose, trehalose, citrate, malate, glutamine, or glutamic acid was selected as the carbon source treatment because these compounds were detected at high concentrations in hyphal exudates. Glucose was used as the control. There were 8 treatments in total. The same liquid MSR medium [17 (link)] as that used for the collection of hyphal exudates (see Supplementary Information) was used to dissolved specific carbon source. The carbon and nitrogen contents in the medium were adjusted to the same level as those in the hyphal exudate solutions (7.16 mM C and 2.35 mM N). The hyphal exudate medium and specific compound medium were supplemented with 10% FeNaEDTA (relative to MSR medium) to ensure denitrification. The medium pH was then adjusted to 7.2, and the medium was filtered through an Acrodisc syringe filter (0.22-μm Super Membrane, Pall Corporation, Port Washington, NY) to obtain carbon-based medium (CB medium). The CB medium was supplemented with 92.84 mM glucose to reach an initial C concentration of 100 mM. NO₃⁻-N was supplemented to reach a level of 10 mM to ensure denitrification. The pellet obtained from the centrifugation of 1 mL P. fluorescens JL1 suspension was re-suspended in 10 mL modified CB medium and transferred to a 120-mL anaerobic serum bottle. All serum bottles were shaken at 180 rpm and maintained at 30 °C. The gas was measured after 0.5, 1, 2, 3, 6, 8, 10, and 12 h. Each treatment was set up in triplicate. Details are shown in the Supplementary Information.

Li X., Zhao R., Li D., Wang G., Bei S., Ju X., An R., Li L., Kuyper T.W., Christie P., Bender F.S., Veen C., van der Heijden M.G., van der Putten W.H., Zhang F., Butterbach-Bahl K, & Zhang J. (2023). Mycorrhiza-mediated recruitment of complete denitrifying Pseudomonas reduces N2O emissions from soil. Microbiome, 11, 45.

Publication 2023

Biological Assay Carbon Centrifugation Citrates Denitrification Exudate Fructose Glucose Glutamic Acid Glutamine Hyphae malate Nitrogen Serum Syringes Tissue, Membrane Trehalose

16S rRNA Gene Sequencing and Microbial Function Prediction

The raw 16 S rRNA gene sequencing reads were demultiplexed, quality‐filtered by Trimmomatic, and merged by FLASH with the following criteria: (i) the 300 bp reads were truncated at any site receiving an average quality score of <20 over a 50 bp sliding window, and the truncated reads shorter than 50 bp were discarded, reads containing ambiguous characters were also discarded; (ii) only overlapping sequences longer than 10 bp were assembled according to their overlapped sequence. The maximum mismatch ratio of the overlap region is 0.2. Reads that could not be assembled were discarded; (iii) Samples were distinguished according to the barcode and primers, and the sequence direction was adjusted, exact barcode matching, 2 nucleotide mismatch in primer matching.
UPARSE (version 7.1, http://drive5.com/uparse/) was used to cluster operational taxonomic units (OTUs) using a 97% similarity criterion (Edgar et al., 2013 (link)), and chimeric sequences were discovered and eliminated. RDP Classifier (http://rdp.cme.msu.edu/) was used to compare the taxonomy of each OTU representative sequence to the 16S rRNA database, with a confidence level of 0.7.
After loading the normalized bacterial OTUs table into PICRUSt2, the bacterial nitrification and denitrification functions were predicted using the KEGG database (Douglas et al., 2020 (link)). The weighted closest sequenced taxon index (NSTI) scores were used to confirm the accuracy of PICRUSt2 predictions for each sample. The NSTI score is <0.17 (Langille et al., 2013 (link)).

Cao X., Liu H., Liu Y., Jing J., Wen L., Xu Z., Liu X., Liu D., Zhuo Y, & Wang L. (2023). N2O emission associated with shifts of bacterial communities in riparian wetland during the spring thawing periods. Ecology and Evolution, 13(3), e9888.

Publication 2023

Bacteria Character Chimera Denitrification Nitrification Nucleotides Oligonucleotide Primers Operating Tables Ribosomal RNA Genes RNA, Ribosomal, 16S

Investigating Nitrogen Metabolism in Strain 24S4-2

To investigate the nitrogen metabolic pathway of strain 24S4–2, the strain was cultured in different nitrogen source media and its metabolites were examined. A single colony was inoculated into a 5 mL R2A liquid medium to enrich the 24S4–2 strain for denitrification and intracellular nitrate analysis at 160 rpm/20°C for 48 h. The pellet was then washed three times with sterilized water after centrifuging the pre-cultured 24S4–2 strain for 15 min at 10000 rpm. The final pellet was incubated at 20°C under 160 rpm in a basal medium containing 30 mM NaNO₃ or NaNO₂. Using HPLC (Ultimate 3,000, Dionex), nitrate and nitrite concentrations were measured every 12 h to probe the DNRA of strain 24S4–2. In order to study the presence of nitrate in the intracellular liquid, strain 24S4–2 was cultivated for 3 days before being centrifuged at 10000 rpm for 15 min and rinsed three times with sterilized water. The pellet was suspended in 30 mL sterile water and was disrupted 5 times by a high-pressure homogenous cell disruption instrument (FPG 12800, SFP) until the liquid became clear.
An Agilent Eclipse XDB-C18 column (4.6 × 250 mm, 5 μm) was used to separate the samples. The mobile phase consisted of 100% HPLC-grade methanol and 1.25 mM mixed phosphate (containing 3 mM tetrabutylammonium bromide) in a 15:85 ratio, with a column temperature set at 30°C, 230 nm detection wavelength, 20 μL injection volume, and 1.0 mL/min flow rate. A standard curve was used to calculate the nitrate concentrations.
The Griess reaction was used to examine nitrite. The ammonium concentration was evaluated using Nessler’s reagent method (absorption at a wavelength of 420 nm).

Liu Y., Zhang Y., Huang Y., Niu J., Huang J., Peng X, & Peng F. (2023). Spatial and temporal conversion of nitrogen using Arthrobacter sp. 24S4–2, a strain obtained from Antarctica. Frontiers in Microbiology, 14, 1040201.

Publication 2023

Ammonium Cells Denitrification High-Performance Liquid Chromatographies Homozygote Intracellular Fluid Methanol Nitrates Nitrites Nitrogen Phosphates Pressure Protoplasm Sterility, Reproductive Strains tetrabutylammonium bromide

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Denitrification

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